library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.0.0     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ dplyr   0.7.6
## ✔ tidyr   0.8.1     ✔ stringr 1.3.1
## ✔ readr   1.1.1     ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(ggridges)
## 
## Attaching package: 'ggridges'
## The following object is masked from 'package:ggplot2':
## 
##     scale_discrete_manual
library(rnoaa)

weather_df = 
  rnoaa::meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
                      var = c("PRCP", "TMIN", "TMAX"), 
                      date_min = "2017-01-01",
                      date_max = "2017-12-31") %>%
  mutate(
    name = recode(id, USW00094728 = "CentralPark_NY", 
                      USC00519397 = "Waikiki_HA",
                      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10) %>%
  select(name, id, everything())
weather_df
## # A tibble: 1,095 x 6
##    name           id          date        prcp  tmax  tmin
##    <chr>          <chr>       <date>     <dbl> <dbl> <dbl>
##  1 CentralPark_NY USW00094728 2017-01-01     0   8.9   4.4
##  2 CentralPark_NY USW00094728 2017-01-02    53   5     2.8
##  3 CentralPark_NY USW00094728 2017-01-03   147   6.1   3.9
##  4 CentralPark_NY USW00094728 2017-01-04     0  11.1   1.1
##  5 CentralPark_NY USW00094728 2017-01-05     0   1.1  -2.7
##  6 CentralPark_NY USW00094728 2017-01-06    13   0.6  -3.8
##  7 CentralPark_NY USW00094728 2017-01-07    81  -3.2  -6.6
##  8 CentralPark_NY USW00094728 2017-01-08     0  -3.8  -8.8
##  9 CentralPark_NY USW00094728 2017-01-09     0  -4.9  -9.9
## 10 CentralPark_NY USW00094728 2017-01-10     0   7.8  -6  
## # ... with 1,085 more rows

Start to make the plot

Blank plot…..

ggplot(weather_df, aes(x = tmin, y = tmax))

Scatterplot…

ggplot(weather_df, aes(x = tmin, y = tmax))+
  geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).

weather_df %>%
  filter(name == "CentralPank_NY") %>%
  ggplot(aes(x = tmin, y = tmax))+
  geom_point()

weather_sp =
 ggplot(weather_df, aes(x = tmin, y = tmax))+
  geom_point()

More plot options

Add on aesthetic

ggplot(weather_df, aes(x = tmin, y = tmax))+
  geom_point(aes(color = name))
## Warning: Removed 15 rows containing missing values (geom_point).

Add a geom

ggplot(weather_df, aes(x = tmin, y = tmax))+
  geom_point(aes(color = name))+
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

Add some options…..

add a gemo

ggplot(weather_df, aes(x = tmin, y = tmax))+
  geom_point(aes(color = name), alpha = 0.4)+
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

move color code to ggplot from the geom_point

ggplot(weather_df, aes(x = tmin, y = tmax,color = name))+
  geom_point(alpha = 0.4)+
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

Factting…..(divide 3 form)

ggplot(weather_df, aes(x = tmin, y = tmax,color = name))+
  geom_point(alpha = 0.4)+
  geom_smooth(se = FALSE)+
  facet_grid( ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

NO color ….

ggplot(weather_df, aes(x = tmin, y = tmax))+
  geom_point(alpha = 0.4)+
  geom_smooth(se = FALSE)+
  facet_grid( ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

A more interesting plot

ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
  geom_point()
## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
  geom_line()

ggplot(weather_df, aes(x = date, y = tmax, color = name, size = prcp)) +
  geom_point()
## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(weather_df, aes(x = date, y = tmax, color = name, size = prcp)) +
  geom_point()+
  geom_smooth(se = FALSE) +
  facet_grid( ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(weather_df, aes(x = date, y = tmax, color = name, size = prcp)) +
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).

ggplot(weather_df, aes(x = date, y = tmax, color = name, size = prcp)) +
  geom_hex()
## Warning: Removed 3 rows containing non-finite values (stat_binhex).

ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin), color = "blue")
## Warning: Removed 15 rows containing missing values (geom_point).

ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin, color = "blue"))
## Warning: Removed 15 rows containing missing values (geom_point).

Univariable plot

Histrogram!

ggplot(weather_df, aes(x = tmax)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).

ggplot(weather_df, aes(x = tmax)) +
  geom_histogram() +
  facet_grid(~name)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).

ggplot(weather_df, aes(x = tmax, fill = name)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).

Density plot…

ggplot(weather_df, aes(x = tmax, fill = name)) +
  geom_density(alpha=0.5)
## Warning: Removed 3 rows containing non-finite values (stat_density).

Boxplots!!

ggplot(weather_df, aes(x = name, y = tmax)) +
  geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Violin plot…

ggplot(weather_df, aes(x = name, y = tmax)) +
  geom_violin()
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

Ridge plot…

ggplot(weather_df, aes(x = tmax, y = name)) +
  geom_density_ridges()
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).

ggplot(weather_df, aes(x = prcp, y = name)) + 
  geom_density_ridges(scale = .85)
## Picking joint bandwidth of 4.61
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).

Save the ggplot

weather_plot = ggplot(weather_df, aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name), alpha = .5) 

ggsave("weather_plot.pdf", weather_plot, width = 8, height = 5)
## Warning: Removed 15 rows containing missing values (geom_point).